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1.
BMC Med Inform Decis Mak ; 21(1): 113, 2021 04 03.
Artículo en Inglés | MEDLINE | ID: mdl-33812388

RESUMEN

BACKGROUND: Ensuring data is of appropriate quality is essential for the secondary use of electronic health records (EHRs) in research and clinical decision support. An effective method of data quality assessment (DQA) is automating data quality rules (DQRs) to replace the time-consuming, labor-intensive manual process of creating DQRs, which is difficult to guarantee standard and comparable DQA results. This paper presents a case study of automatically creating DQRs based on openEHR archetypes in a Chinese hospital to investigate the feasibility and challenges of automating DQA for EHR data. METHODS: The clinical data repository (CDR) of the Shanxi Dayi Hospital is an archetype-based relational database. Four steps are undertaken to automatically create DQRs in this CDR database. First, the keywords and features relevant to DQA of archetypes were identified via mapping them to a well-established DQA framework, Kahn's DQA framework. Second, the templates of DQRs in correspondence with these identified keywords and features were created in the structured query language (SQL). Third, the quality constraints were retrieved from archetypes. Fourth, these quality constraints were automatically converted to DQRs according to the pre-designed templates and mapping relationships of archetypes and data tables. We utilized the archetypes of the CDR to automatically create DQRs to meet quality requirements of the Chinese Application-Level Ranking Standard for EHR Systems (CARSES) and evaluated their coverage by comparing with expert-created DQRs. RESULTS: We used 27 archetypes to automatically create 359 DQRs. 319 of them are in agreement with the expert-created DQRs, covering 84.97% (311/366) requirements of the CARSES. The auto-created DQRs had varying levels of coverage of the four quality domains mandated by the CARSES: 100% (45/45) of consistency, 98.11% (208/212) of completeness, 54.02% (57/87) of conformity, and 50% (11/22) of timeliness. CONCLUSION: It's feasible to create DQRs automatically based on openEHR archetypes. This study evaluated the coverage of the auto-created DQRs to a typical DQA task of Chinese hospitals, the CARSES. The challenges of automating DQR creation were identified, such as quality requirements based on semantic, and complex constraints of multiple elements. This research can enlighten the exploration of DQR auto-creation and contribute to the automatic DQA.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas , Registros Electrónicos de Salud , Exactitud de los Datos , Humanos , Lenguaje , Semántica
4.
Am J Nurs ; 121(4): 16, 2021 04 01.
Artículo en Inglés | MEDLINE | ID: mdl-33755610
5.
BMC Med Inform Decis Mak ; 21(1): 93, 2021 03 09.
Artículo en Inglés | MEDLINE | ID: mdl-33750371

RESUMEN

BACKGROUND: Assessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results. OBJECTIVES: To present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations. METHODS: We describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool-openCQA-that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models. RESULTS: Applying the method on the study's dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment. CONCLUSIONS: The main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.


Asunto(s)
Exactitud de los Datos , Registros Electrónicos de Salud , Humanos , Bases del Conocimiento
6.
CMAJ Open ; 9(1): E261-E270, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33731427

RESUMEN

BACKGROUND: Emergency physicians lack high-quality evidence for many diagnostic and treatment decisions made for patients with suspected or confirmed coronavirus disease 2019 (COVID-19). Our objective is to describe the methods used to collect and ensure the data quality of a multicentre registry of patients presenting to the emergency department with suspected or confirmed COVID-19. METHODS: This methodology study describes a population-based registry that has been enrolling consecutive patients presenting to the emergency department with suspected or confirmed COVID-19 since Mar. 1, 2020. Most data are collected from retrospective chart review. Phone follow-up with patients at 30 days captures the World Health Organization clinical improvement scale and contextual, social and cultural variables. Phone follow-up also captures patient-reported quality of life using the Veterans Rand 12-Item Health Survey at 30 days, 60 days, 6 months and 12 months. Fifty participating emergency departments from 8 provinces in Canada currently enrol patients into the registry. INTERPRETATION: Data from the registry of the Canadian COVID-19 Emergency Department Rapid Response Network will be used to derive and validate clinical decision rules to inform clinical decision-making, describe the natural history of the disease, evaluate COVID-19 diagnostic tests and establish the real-world effectiveness of treatments and vaccines, including in populations that are excluded or underrepresented in clinical trials. This registry has the potential to generate scientific evidence to inform our pandemic response, and to serve as a model for the rapid implementation of population-based data collection protocols for future public health emergencies. TRIAL REGISTRATION: Clinicaltrials.gov, no. NCT04702945.


Asunto(s)
Medicina de Emergencia , Sistema de Registros , /diagnóstico , Canadá , Exactitud de los Datos , Recolección de Datos , Manejo de Datos , Servicio de Urgencia en Hospital , Medicina de Emergencia Basada en la Evidencia , Estudios de Seguimiento , Humanos , Almacenamiento y Recuperación de la Información , Medición de Resultados Informados por el Paciente , Estudios Prospectivos , Calidad de Vida , Estudios Retrospectivos , Teléfono
8.
Front Public Health ; 9: 645229, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33768087

RESUMEN

Credible, reliable and consistent information to the public, as well as health professionals and decision makers, is crucial to help navigate uncertainty and risk in times of crisis and concern. Traditionally, information and health communications issued by respected and established government agencies have been regarded as factual, unbiased and credible. The U.S. Centers for Disease Control and Prevention (CDC) is such an agency that addresses all aspects of health and public health on behalf of the U.S Government for the benefit of its citizens. In July 2020, the CDC issued guidelines on reopening schools which resulted in open criticism by the U.S. President and others, prompting a review and publication of revised guidelines together with a special "Statement on the Importance of Reopening Schools under COVID-19." We hypothesize that this statement introduced bias with the intention to shift the public perception and media narrative in favor of reopening of schools. Using a mixed methods approach, including an online text analysis tool, we demonstrate that document title and structure, word frequencies, word choice, and website presentation did not provide a balanced account of the complexity and uncertainty surrounding school reopening during the COVID-19 pandemic. Despite available scientific guidance and practical evidence-based advice on how to manage infection risks when reopening schools, the CDC Statement was intentionally overriding possible parent and public health concerns. The CDC Statement provides an example of how political influence is exercised over the presentation of science in the context of a major pandemic. It was withdrawn by the CDC in November 2020.


Asunto(s)
Centers for Disease Control and Prevention, U.S./normas , Guías como Asunto , Política de Salud , Salud Pública/estadística & datos numéricos , Salud Pública/normas , Instituciones Académicas/estadística & datos numéricos , Estudiantes/estadística & datos numéricos , Adolescente , Adulto , Niño , Preescolar , Exactitud de los Datos , Análisis de Datos , Femenino , Humanos , Masculino , Persona de Mediana Edad , Pandemias , Estados Unidos/epidemiología , Adulto Joven
9.
J Biomed Inform ; 116: 103715, 2021 04.
Artículo en Inglés | MEDLINE | ID: mdl-33610878

RESUMEN

Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response.


Asunto(s)
/epidemiología , Registros Electrónicos de Salud , Pandemias , /mortalidad , California/epidemiología , Exactitud de los Datos , Prestación Integrada de Atención de Salud/estadística & datos numéricos , Registros Electrónicos de Salud/estadística & datos numéricos , Intercambio de Información en Salud/estadística & datos numéricos , Capacidad de Camas en Hospitales/estadística & datos numéricos , Humanos , Difusión de la Información/métodos , Informática Médica , Pandemias/estadística & datos numéricos
10.
Nat Commun ; 12(1): 943, 2021 02 11.
Artículo en Inglés | MEDLINE | ID: mdl-33574258

RESUMEN

The COVID-19 pandemic began in early 2020 with major health consequences. While a need to disseminate information to the medical community and general public was paramount, concerns have been raised regarding the scientific rigor in published reports. We performed a systematic review to evaluate the methodological quality of currently available COVID-19 studies compared to historical controls. A total of 9895 titles and abstracts were screened and 686 COVID-19 articles were included in the final analysis. Comparative analysis of COVID-19 to historical articles reveals a shorter time to acceptance (13.0[IQR, 5.0-25.0] days vs. 110.0[IQR, 71.0-156.0] days in COVID-19 and control articles, respectively; p < 0.0001). Furthermore, methodological quality scores are lower in COVID-19 articles across all study designs. COVID-19 clinical studies have a shorter time to publication and have lower methodological quality scores than control studies in the same journal. These studies should be revisited with the emergence of stronger evidence.


Asunto(s)
Exactitud de los Datos , Publicaciones Periódicas como Asunto , Animales , Estudios Clínicos como Asunto , Humanos , Pandemias , Revisión de la Investigación por Pares , Proyectos de Investigación , Factores de Tiempo
11.
Sci Rep ; 11(1): 4145, 2021 02 18.
Artículo en Inglés | MEDLINE | ID: mdl-33603047

RESUMEN

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.


Asunto(s)
/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , /epidemiología , China/epidemiología , Exactitud de los Datos , Aprendizaje Profundo , Humanos , Pulmón/patología , Neumonía/diagnóstico por imagen , Estudios Retrospectivos , Sensibilidad y Especificidad
12.
J Perinat Med ; 49(3): 255-261, 2021 Mar 26.
Artículo en Inglés | MEDLINE | ID: mdl-33554570

RESUMEN

OBJECTIVES: Fever is the single most frequently reported manifestation of COVID-19 and is a critical element of screening persons for COVID-19. The meaning of "fever" varies depending on the cutoff temperature used, the type of thermometer, the time of the day, the site of measurements, and the person's gender and race. The absence of a universally accepted definition for fever has been especially problematic during the current COVID-19 pandemic. METHODS: This investigation determined the extent to which fever is defined in COVID-19 publications, with special attention to those associated with pregnancy. RESULTS: Of 53 publications identified in which "fever" is reported as a manifestation of COVID-19 illness, none described the method used to measure patient's temperatures. Only 10 (19%) publications specified the minimum temperature used to define a fever with values that varied from a 37.3 °C (99.1 °F) to 38.1 °C (100.6 °F). CONCLUSIONS: There is a disturbing lack of precision in defining fever in COVID-19 publications. Given the many factors influencing temperature measurements in humans, there can never be a single, universally accepted temperature cut-off defining a fever. This clinical reality should not prevent precision in reporting fever. To achieve the precision and improve scientific and clinical communication, when fever is reported in clinical investigations, at a minimum the cut-off temperature used in determining the presence of fever, the anatomical site at which temperatures are taken, and the instrument used to measure temperatures should each be described. In the absence of such information, what is meant by the term "fever" is uncertain.


Asunto(s)
/métodos , Exactitud de los Datos , Fiebre/diagnóstico , Publicaciones Periódicas como Asunto , Proyectos de Investigación/normas , Termometría/normas , /complicaciones , /normas , Femenino , Fiebre/virología , Humanos , Embarazo , Complicaciones Infecciosas del Embarazo/diagnóstico , Estándares de Referencia , Proyectos de Investigación/estadística & datos numéricos , Termómetros , Termometría/instrumentación , Termometría/métodos
13.
Natl Vital Stat Rep ; 69(14): 1-25, 2021 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-33541519

RESUMEN

Objectives-This report expands the measures used to evaluate cause-of-death data quality by presenting a novel list of unsuitable underlying causes of death (UCOD). This list is intended to facilitate the measurement of the quality of cause-of-death reporting by medical certifiers in terms of completeness, as assessed by a UCOD that is sufficiently specific. Methods-A list of codes from the International Statistical Classification of Diseases and Related Health Problems, 10th Revision was developed to classify unsuitable UCODs defined according to three main subtypes: unknown and ill-defined causes, immediate and intermediate causes, and nonspecific UCODs. Unsuitable UCODs and the three subtypes were examined using 2018 death certificate data for both U.S. residents and nonresidents in the 50 states and the District of Columbia. Differences in the frequency of unsuitable UCODs and the subtypes were tested by age group, place of death, and state of occurrence. Trends in unsuitable UCODs and the three subtypes were also investigated by analyzing death certificate data from 2010 to 2018. Results-In 2018, 34.7% of all death records had an unsuitable UCOD: 2.2% had an unknown or ill-defined cause as the UCOD, 12.7% had an immediate or intermediate cause as the UCOD, and 19.8% had a nonspecific UCOD. Unsuitable UCODs and the subtypes varied by age group, place of death, state, and year. No trend in unsuitable UCODs from 2010 to 2013 was seen, but from 2013 to 2018, a decrease of 0.6% per year was observed, which is likely due to a similar decrease in nonspecific UCODs during the same time period. Conclusion-This novel list of unsuitable UCOD codes can be used to assess the quality of cause-of-death data over time and by other various characteristics, with further applications for efforts to improve mortality data quality.


Asunto(s)
Causas de Muerte , Exactitud de los Datos , Certificado de Defunción , Humanos , Estados Unidos/epidemiología , Estadísticas Vitales
14.
J Med Internet Res ; 23(1): e21382, 2021 01 22.
Artículo en Inglés | MEDLINE | ID: mdl-33480859

RESUMEN

BACKGROUND: A population-level survey (PLS) is an essential and standard method used in public health research that supports the quantification of sociodemographic events, public health policy development, and intervention designs. Data collection mechanisms in PLS seem to be a significant determinant in avoiding mistakes. Using electronic devices such as smartphones and tablet computers improves the quality and cost-effectiveness of public health surveys. However, there is a lack of systematic evidence to show the potential impact of electronic data collection tools on data quality and cost reduction in interviewer-administered surveys compared with the standard paper-based data collection system. OBJECTIVE: This systematic review aims to evaluate the impact of the interviewer-administered electronic data collection methods on data quality and cost reduction in PLS compared with traditional methods. METHODS: We conducted a systematic search of MEDLINE, CINAHL, PsycINFO, the Web of Science, EconLit, Cochrane CENTRAL, and CDSR to identify relevant studies from 2008 to 2018. We included randomized and nonrandomized studies that examined data quality and cost reduction outcomes, as well as usability, user experience, and usage parameters. In total, 2 independent authors screened the title and abstract, and extracted data from selected papers. A third author mediated any disagreements. The review authors used EndNote for deduplication and Rayyan for screening. RESULTS: Our search produced 3817 papers. After deduplication, we screened 2533 papers, and 14 fulfilled the inclusion criteria. None of the studies were randomized controlled trials; most had a quasi-experimental design, for example, comparative experimental evaluation studies nested on other ongoing cross-sectional surveys. A total of 4 comparative evaluations, 2 pre-post intervention comparative evaluations, 2 retrospective comparative evaluations, and 4 one-arm noncomparative studies were included. Meta-analysis was not possible because of the heterogeneity in study designs, types, study settings, and level of outcome measurements. Individual paper synthesis showed that electronic data collection systems provided good quality data and delivered faster compared with paper-based data collection systems. Only 2 studies linked cost and data quality outcomes to describe the cost-effectiveness of electronic data collection systems. Field data collectors reported that an electronic data collection system was a feasible, acceptable, and preferable tool for their work. Onsite data error prevention, fast data submission, and easy-to-handle devices were the comparative advantages offered by electronic data collection systems. Challenges during implementation included technical difficulties, accidental data loss, device theft, security concerns, power surges, and internet connection problems. CONCLUSIONS: Although evidence exists of the comparative advantages of electronic data collection compared with paper-based methods, the included studies were not methodologically rigorous enough to combine. More rigorous studies are needed to compare paper and electronic data collection systems in public health surveys considering data quality, work efficiency, and cost reduction. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/10678.


Asunto(s)
Análisis Costo-Beneficio/normas , Exactitud de los Datos , Encuestas Epidemiológicas/economía , Salud Pública/economía , Salud Pública/métodos , Estudios Transversales , Humanos , Estudios Retrospectivos
15.
BMC Med Inform Decis Mak ; 21(1): 3, 2021 01 06.
Artículo en Inglés | MEDLINE | ID: mdl-33407429

RESUMEN

BACKGROUND: Next-generation sequencing provides comprehensive information about individuals' genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in the clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients' genetic information and further associate treatment decisions with genetic information. METHODS: We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated in a Foundation-tested women cancer cohort (N = 196). Upon retrieval of patients' genetic information using NLP system, we assessed the completeness of genetic data captured in unstructured clinical notes according to a genetic data-model. We examined the distribution of different topics regarding BRCA1/2 throughout patients' treatment process, and then analyzed the association between BRCA1/2 mutation status and the discussion/prescription of targeted therapy. RESULTS: We identified seven topics in the clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance. Our rule-based system achieved a precision of 0.87, recall of 0.93 and F-measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F-measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F-measure of 0.82 for seven-topic classification. We found in result-containing sentences, the capture of BRCA1/2 mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between BRCA1/2 positive mutation and targeted therapies. CONCLUSIONS: In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes. Data quality issues such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate the real-world utility of genetic information to initiate a prescription of targeted therapy.


Asunto(s)
Registros Electrónicos de Salud , Procesamiento de Lenguaje Natural , Exactitud de los Datos , Femenino , Humanos , Lenguaje , Aprendizaje Automático
16.
BMC Infect Dis ; 21(1): 49, 2021 Jan 11.
Artículo en Inglés | MEDLINE | ID: mdl-33430790

RESUMEN

BACKGROUND: The World Health Organization (WHO) has endorsed the next-generation Xpert MTB/RIF Ultra (Ultra) cartridge, and Uganda is currently transitioning from the older generation Xpert MTB/RIF (Xpert) cartridge to Ultra as the initial diagnostic test for pulmonary tuberculosis (TB). We assessed the diagnostic accuracy of Ultra for pulmonary TB among adults in Kampala, Uganda. METHODS: We sampled adults referred for Xpert testing at two hospitals and a health center over a 12-month period. We enrolled adults with positive Xpert and a random 1:1 sample with negative Xpert results. Expectorated sputum was collected for Ultra, and for solid and liquid culture testing for Xpert-negative patients. We measured sensitivity and specificity of Ultra overall and by HIV status, prior history of TB, and hospitalization, in reference to Xpert and culture results. We also assessed how classification of results in the new "trace" category affects Ultra accuracy. RESULTS: Among 698 participants included, 211 (30%) were HIV-positive and 336 (48%) had TB. The sensitivity of Ultra was 90.5% (95% CI 86.8-93.4) and specificity was 98.1% (95% CI 96.1-99.2). There were no significant differences in sensitivity and specificity by HIV status, prior history of TB or hospitalization. Xpert and Ultra results were concordant in 670 (96%) participants, with Ultra having a small reduction in specificity (difference 1.9, 95% CI 0.2 to 3.6, p=0.01). When "trace" results were considered positive for all patients, sensitivity increased by 2.1% (95% CI 0.3 to 3.9, p=0.01) without a significant reduction in specificity (- 0.8, 95% CI - 0.3 to 2.0, p=0.08). CONCLUSIONS: After 1 year of implementation, Ultra had similar performance to Xpert. Considering "trace" results to be positive in all patients increased case detection without significant loss of specificity. Longitudinal studies are needed to compare the benefit of greater diagnoses to the cost of overtreatment.


Asunto(s)
Exactitud de los Datos , Mycobacterium tuberculosis/genética , Técnicas de Amplificación de Ácido Nucleico/métodos , Tuberculosis Pulmonar/diagnóstico , Tuberculosis Pulmonar/epidemiología , Adulto , Estudios Transversales , Femenino , VIH/genética , Infecciones por VIH/diagnóstico , Infecciones por VIH/virología , Humanos , Masculino , Prevalencia , Sensibilidad y Especificidad , Esputo/microbiología , Tuberculosis Pulmonar/microbiología , Uganda/epidemiología
17.
BMC Infect Dis ; 21(1): 63, 2021 Jan 13.
Artículo en Inglés | MEDLINE | ID: mdl-33435896

RESUMEN

BACKGROUND: Chest X-ray (CXR) interpretation remains a central component of the current World Health Organization recommendations as an adjuvant test in diagnosis of smear-negative tuberculosis (TB). With its low specificity, high maintenance and operational costs, utility of CXR in diagnosis of smear-negative TB in high HIV/TB burden settings in the Xpert MTB/RIF era remains unpredictable. We evaluated accuracy and additive value of CXR to Xpert MTB/RIF in the diagnosis of TB among HIV-positive smear-negative presumptive TB patients. METHODS: HIV co-infected presumptive TB patients were recruited from the Infectious Diseases Institute outpatient clinic and in-patient medical wards of Mulago Hospital, Uganda. CXR films were reviewed by two independent radiologists using a standardized evaluation form. CXR interpretation with regard to TB was either positive (consistent with TB) or negative (normal or unlikely TB). Sensitivity, specificity and predictive values of CXR and CXR combined with Xpert MTB/RIF for diagnosis of smear-negative TB in HIV-positive patients were calculated using sputum and/or blood mycobacterial culture as reference standard. RESULTS: Three hundred sixty-six HIV co-infected smear-negative participants (female, 63.4%; hospitalized, 68.3%) had technically interpretable CXR. Median (IQR) age was 32 (28-39) years and CD4 count 112 (23-308) cells/mm3. Overall, 22% (81/366) were positive for Mycobacterium tuberculosis (Mtb) on culture; 187/366 (51.1%) had CXR interpreted as consistent with TB, of which 55 (29.4%) had culture-confirmed TB. Sensitivity and specificity of CXR interpretation in diagnosis of culture-positive TB were 67.9% (95%CI 56.6-77.8) and 53.7% (95%CI 47.7-59.6) respectively, while Xpert MTB/RIF sensitivity and specificity were 65.4% (95%CI 54.0-75.7) and 95.8% (95%CI 92.8-97.8) respectively. Addition of CXR to Xpert MTB/RIF had overall sensitivity and specificity of 87.7% (95%CI 78.5-93.9) and 51.6% (95%CI 45.6-57.5) respectively; 86.2% (95%CI 75.3-93.5) and 48.1% (95%CI 40.7-55.6) among inpatients and 93.8% (95%CI 69.8-99.8) and 58.0% (95%CI 47.7-67.8) among outpatients respectively. CONCLUSION: In this high prevalence TB/HIV setting, CXR interpretation added sensitivity to Xpert MTB/RIF test at the expense of specificity in the diagnosis of culture-positive TB in HIV-positive individuals presenting with TB symptoms and negative smear. CXR interpretation may not add diagnostic value in settings where Xpert MTB/RIF is available as a TB diagnostic tool.


Asunto(s)
Infecciones Oportunistas Relacionadas con el SIDA/complicaciones , Coinfección/diagnóstico , VIH/aislamiento & purificación , Radiografías Pulmonares Masivas/métodos , Mycobacterium tuberculosis/genética , Técnicas de Amplificación de Ácido Nucleico/métodos , Tuberculosis Pulmonar/complicaciones , Tuberculosis Pulmonar/diagnóstico , Infecciones Oportunistas Relacionadas con el SIDA/epidemiología , Infecciones Oportunistas Relacionadas con el SIDA/virología , Adulto , Recuento de Linfocito CD4 , Coinfección/epidemiología , Coinfección/virología , Exactitud de los Datos , Femenino , Recursos en Salud , Humanos , Masculino , Sensibilidad y Especificidad , Esputo/microbiología , Tuberculosis Pulmonar/epidemiología , Tuberculosis Pulmonar/microbiología , Uganda/epidemiología
18.
BMC Infect Dis ; 21(1): 77, 2021 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-33451284

RESUMEN

BACKGROUND: Candidemia has emerged as an important nosocomial infection, with a mortality rate of 30-50%. It is the fourth most common nosocomial bloodstream infection (BSI) in the United States and the seventh most common nosocomial BSI in Europe and Japan. The aim of this study was to assess the performance of the Sequential Organ Failure Assessment (SOFA) score for determining the severity and prognosis of candidemia. METHODS: We performed a retrospective study of patients admitted to hospital with candidemia between September 2014 and May 2018. The severity of candidemia was evaluated using the SOFA score and the Acute Physiology, Age, Chronic Health Evaluation II (APACHE II) score. Patients' underlying diseases were assessed by the Charlson Comorbidity Index (CCI). RESULTS: Of 70 patients enrolled, 41 (59%) were males, and 29 (41%) were females. Their median age was 73 years (range: 36-93 years). The most common infection site was catheter-related bloodstream infection (n=36, 51%).The 30-day, and in-hospital mortality rates were 36 and 43%, respectively. Univariate analysis showed that SOFA score ≥5, APACHE II score ≥13, initial antifungal treatment with echinocandin, albumin < 2.3, C-reactive protein > 6, disturbance of consciousness, and CCI ≥3 were related with 30-day mortality. Of these 7, multivariate analysis showed that the combination of SOFA score ≥5 and CCI ≥3 was the best independent prognostic indicator for 30-day and in-hospital mortality. CONCLUSIONS: The combined SOFA score and CCI was a better predictor of the 30-day mortality and in-hospital mortality than the APACHE II score alone.


Asunto(s)
APACHE , Candidemia/diagnóstico , Candidemia/mortalidad , Infección Hospitalaria/diagnóstico , Infección Hospitalaria/mortalidad , Exactitud de los Datos , Puntuaciones en la Disfunción de Órganos , Adulto , Anciano , Anciano de 80 o más Años , Proteína C-Reactiva/análisis , Candidemia/epidemiología , Candidemia/patología , Comorbilidad , Infección Hospitalaria/epidemiología , Infección Hospitalaria/patología , Femenino , Mortalidad Hospitalaria , Humanos , Japón/epidemiología , Masculino , Persona de Mediana Edad , Pronóstico , Curva ROC , Estudios Retrospectivos
19.
Anal Chem ; 93(4): 2669-2677, 2021 02 02.
Artículo en Inglés | MEDLINE | ID: mdl-33465307

RESUMEN

Existing data acquisition modes such as full-scan, data-dependent (DDA), and data-independent acquisition (DIA) often present limited capabilities in capturing metabolic information in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. In this work, we proposed a novel metabolomic data acquisition workflow that combines DDA and DIA analyses to achieve better metabolomic data quality, including enhanced metabolome coverage, tandem mass spectrometry (MS2) coverage, and MS2 quality. This workflow, named data-dependent-assisted data-independent acquisition (DaDIA), performs untargeted metabolomic analysis of individual biological samples using DIA mode and the pooled quality control (QC) samples using DDA mode. This combination takes advantage of the high-feature number and MS2 spectral coverage of the DIA data and the high MS2 spectral quality of the DDA data. To analyze the heterogeneous DDA and DIA data, we further developed a computational program, DaDIA.R, to automatically extract metabolic features and perform streamlined metabolite annotation of DaDIA data set. Using human urine samples, we demonstrated that the DaDIA workflow delivers remarkably improved data quality when compared to conventional DDA or DIA metabolomics. In particular, both the number of detected features and annotated metabolites were greatly increased. Further biological demonstration using a leukemia metabolomics study also proved that the DaDIA workflow can efficiently detect and annotate around 4 times more significant metabolites than DDA workflow with broad MS2 coverage and high MS2 spectral quality for downstream statistical analysis and biological interpretation. Overall, this work represents a critical development of data acquisition mode in untargeted metabolomics, which can greatly benefit untargeted metabolomics for a wide range of biological applications.


Asunto(s)
Exactitud de los Datos , Metabolómica/métodos , Programas Informáticos , Humanos , Leucemia/metabolismo , Metaboloma , Urinálisis , Flujo de Trabajo
20.
BMJ Glob Health ; 6(1)2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-33419929

RESUMEN

In-person interactions have traditionally been the gold standard for qualitative data collection. The COVID-19 pandemic required researchers to consider if remote data collection can meet research objectives, while retaining the same level of data quality and participant protections. We use four case studies from the Philippines, Zambia, India and Uganda to assess the challenges and opportunities of remote data collection during COVID-19. We present lessons learned that may inform practice in similar settings, as well as reflections for the field of qualitative inquiry in the post-COVID-19 era. Key challenges and strategies to overcome them included the need for adapted researcher training in the use of technologies and consent procedures, preparation for abbreviated interviews due to connectivity concerns, and the adoption of regular researcher debriefings. Participant outreach to allay suspicions ranged from communicating study information through multiple channels to highlighting associations with local institutions to boost credibility. Interviews were largely successful, and contained a meaningful level of depth, nuance and conviction that allowed teams to meet study objectives. Rapport still benefitted from conventional interviewer skills, including attentiveness and fluency with interview guides. While differently abled populations may encounter different barriers, the included case studies, which varied in geography and aims, all experienced more rapid recruitment and robust enrollment. Reduced in-person travel lowered interview costs and increased participation among groups who may not have otherwise attended. In our view, remote data collection is not a replacement for in-person endeavours, but a highly beneficial complement. It may increase accessibility and equity in participant contributions and lower costs, while maintaining rich data collection in multiple study target populations and settings.


Asunto(s)
Recolección de Datos , Relaciones Interpersonales , África del Sur del Sahara , Exactitud de los Datos , Recolección de Datos/métodos , Recolección de Datos/normas , Humanos , India , Internet , Pandemias , Filipinas , Investigación Cualitativa
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